Automatic Traffic Queue-End Identification using Location-Based Waze User Reports

2021 ◽  
pp. 036119812110133
Author(s):  
Yuandong Liu ◽  
Zhihua Zhang ◽  
Lee D. Han ◽  
Candace Brakewood
Keyword(s):  
Real Time ◽  
High Speed ◽  
Recurrent Events ◽  
Clustering Algorithm ◽  
Selection Procedure ◽  
The United States ◽  
Streaming Data ◽  
Data Sources ◽  
Sensor Data ◽  
Temporal Extent

Traffic queues, especially queues caused by non-recurrent events such as incidents, are unexpected to high-speed drivers approaching the end of queue (EOQ) and become safety concerns. Though the topic has been extensively studied, the identification of EOQ has been limited by the spatial-temporal resolution of traditional data sources. This study explores the potential of location-based crowdsourced data, specifically Waze user reports. It presents a dynamic clustering algorithm that can group the location-based reports in real time and identify the spatial-temporal extent of congestion as well as the EOQ. The algorithm is a spatial-temporal extension of the density-based spatial clustering of applications with noise (DBSCAN) algorithm for real-time streaming data with an adaptive threshold selection procedure. The proposed method was tested with 34 traffic congestion cases in the Knoxville,Tennessee area of the United States. It is demonstrated that the algorithm can effectively detect spatial-temporal extent of congestion based on Waze report clusters and identify EOQ in real-time. The Waze report-based detection are compared to the detection based on roadside sensor data. The results are promising: The EOQ identification time of Waze is similar to the EOQ detection time of traffic sensor data, with only 1.1 min difference on average. In addition, Waze generates 1.9 EOQ detection points every mile, compared to 1.8 detection points generated by traffic sensor data, suggesting the two data sources are comparable in respect of reporting frequency. The results indicate that Waze is a valuable complementary source for EOQ detection where no traffic sensors are installed.

scholarly journals Fossel: Efficient Latency Reduction in Approximating Streaming Sensor Data

2020 ◽  
Vol 12 (23) ◽  
pp. 10175
Author(s):  
Fatima Abdullah ◽  
Limei Peng ◽  
Byungchul Tak
Keyword(s):  
Real Time ◽  
Data Analytics ◽  
Streaming Data ◽  
Sensor Data ◽  
Simple Type ◽  
Data Sampling ◽  
Time Data ◽  
Latency Reduction ◽  
Network Delays ◽  
Real Time Data

The volume of streaming sensor data from various environmental sensors continues to increase rapidly due to wider deployments of IoT devices at much greater scales than ever before. This, in turn, causes massive increase in the fog, cloud network traffic which leads to heavily delayed network operations. In streaming data analytics, the ability to obtain real time data insight is crucial for computational sustainability for many IoT enabled applications such as environmental monitors, pollution and climate surveillance, traffic control or even E-commerce applications. However, such network delays prevent us from achieving high quality real-time data analytics of environmental information. In order to address this challenge, we propose the Fog Sampling Node Selector (Fossel) technique that can significantly reduce the IoT network and processing delays by algorithmically selecting an optimal subset of fog nodes to perform the sensor data sampling. In addition, our technique performs a simple type of query executions within the fog nodes in order to further reduce the network delays by processing the data near the data producing devices. Our extensive evaluations show that Fossel technique outperforms the state-of-the-art in terms of latency reduction as well as in bandwidth consumption, network usage and energy consumption.

Kinematic SAMI : a new real-time multi-sensor data assimilation strategy for nonlinear modal identification

2020 ◽  
Vol 21 (4) ◽  
pp. 413
Author(s):  
Adrien Goeller ◽  
Jean-Luc Dion ◽  
Ronan Le Breton ◽  
Thierry Soriano
Keyword(s):  
Real Time ◽  
High Speed ◽  
Natural Frequencies ◽  
Modal Identification ◽  
Modal Testing ◽  
Sensor Data ◽  
Contactless Measurement ◽  
Vibration Level ◽  
Set Up ◽  
Real Time Identification

In many engineering applications, the vibration analysis of a structure requires the set up of a large number of sensors. These studies are mostly performed in post processing and based on linear modal analysis. However, many studied devices highlight that modal parameters depend on the vibration level non linearities and are performed with sensors as accelerometers that modify the dynamics of the device. This work proposes a significant evolution of modal testing based on the real time identification of non linear parameters (natural frequencies and damping) tracked with a linear modal basis. This method, called Kinematic-SAMI (for multiSensors Assimilation Modal Identification) is assessed firstly on a numerical case with known non linearities and secondly in the framework of a classical cantilever beam with contactless measurement technique (high speed and high resolution cameras). Finally, the efficiency and the limits of the method are discussed.

Air pollution analysis using enhanced K-Means clustering algorithm for real time sensor data

2016 ◽  
Author(s):  
Grace R. Kingsy ◽  
R. Manimegalai ◽  
Devasena M.S. Geetha ◽  
S. Rajathi ◽  
K. Usha ◽  
...  
Keyword(s):  
Air Pollution ◽  
Real Time ◽  
Clustering Algorithm ◽  
Sensor Data

CMOS Real-Time Image Acquisition and Display System Design Based on FPGA

2014 ◽  
Vol 644-650 ◽  
pp. 4403-4406
Author(s):  
Jian Wei Leng ◽  
Ying Hui Wu
Keyword(s):  
Data Acquisition ◽  
System Design ◽  
Real Time ◽  
High Speed ◽  
Image Acquisition ◽  
Control Unit ◽  
Image Sensor ◽  
Acquisition System ◽  
Sensor Data ◽  
Time Data

Based on characteristics of image acquisition system of high-speed and large-capacity, this paper presents a CMOS Image sensor data acquisition system that is using FPGA Chip as its core processing devices. Data acquisition logic control unit is designed by FPGA. The modular structure of the system design, FIFO, ping-pong and other technology are used in the design process to ensure real-time data acquisition and transmission. FPGA implementation of video acquisition can improve system performance. It also has a strong adaptability and flexibility, and it is easy to design, debug and so on. Through the experiment, we can get a clear image.

scholarly journals Software Systems Approach to Multi-Scale GIS-BIM Utility Infrastructure Network Integration and Resource Flow Simulation

2018 ◽  
Vol 7 (8) ◽  
pp. 310 ◽  
Author(s):  
Thomas Gilbert ◽  
Stuart Barr ◽  
Philip James ◽  
Jeremy Morley ◽  
Qingyuan Ji
Keyword(s):  
Real Time ◽  
Systems Approach ◽  
Spatiotemporal Dynamics ◽  
Service Planning ◽  
Streaming Data ◽  
Sensor Data ◽  
Integrated Modelling ◽  
Software Systems ◽  
Resource Flows ◽  
Infrastructure Network

There is an increasing impetus for the use of digital city models and sensor network data to understand the current demand for utility resources and inform future infrastructure service planning across a range of spatial scales. Achieving this requires the ability to represent a city as a complex system of connected and interdependent components in which the topology of the electricity, water, gas, and heat demand-supply networks are modelled in an integrated manner. However, integrated modelling of these networks is hampered by the disparity between the predominant data formats and modelling processes used in the Geospatial Information Science (GIS) and Building Information Modelling (BIM) domains. This paper presents a software systems approach to scale-free, multi-format, integrated modelling of evolving cross-domain utility infrastructure network topologies, and the analysis of the spatiotemporal dynamics of their resource flows. The system uses a graph database to integrate the topology of utility network components represented in the CityGML UtilityNetwork Application Domain Extension (ADE), Industry Foundation Classes (IFC) and JavaScript Object Notation (JSON) real-time streaming messages. A message broker is used to disseminate the changing state of the integrated topology and the dynamic resource flows derived from the streaming data. The capability of the developed system is demonstrated via a case study in which internal building and local electricity distribution feeder networks are integrated, and a real-time building management sensor data stream is used to simulate and visualise the spatiotemporal dynamics of electricity flows using a dynamic web-based visualisation.

scholarly journals A Stream Processing System for Multisource Heterogeneous Sensor Data

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Liang Hu ◽  
Rui Sun ◽  
Feng Wang ◽  
Xiuhong Fei ◽  
Kuo Zhao
Keyword(s):  
Data Integration ◽  
Data Processing ◽  
Real Time ◽  
Rapid Development ◽  
Streaming Data ◽  
Sensor Data ◽  
Low Latency ◽  
Time System ◽  
Real Time System ◽  
Set Up

With the rapid development of the Internet of Things (IoT), a variety of sensor data are generated around everyone’s life. New research perspective regarding the streaming sensor data processing of the IoT has been raised as a hot research topic that is precisely the theme of this paper. Our study serves to provide guidance regarding the practical aspects of the IoT. Such guidance is rarely mentioned in the current research in which the focus has been more on theory and less on issues describing how to set up a practical system. In our study, we employ numerous open source projects to establish a distributed real time system to process streaming data of the IoT. Two urgent issues have been solved in our study that are (1) multisource heterogeneous sensor data integration and (2) processing streaming sensor data in real time manner with low latency. Furthermore, we set up a real time system to process streaming heterogeneous sensor data from multiple sources with low latency. Our tests are performed using field test data derived from environmental monitoring sensor data collected from indoor environment for system validation. The results show that our proposed system is valid and efficient for multisource heterogeneous sensor data integration and streaming data processing in real time manner.

scholarly journals A local algorithm to approximate the global clustering of streams generated in ubiquitous sensor networks

2018 ◽  
Vol 14 (10) ◽  
pp. 155014771880823 ◽  
Author(s):  
Pedro Pereira Rodrigues ◽  
João Araújo ◽  
João Gama ◽  
Luís Lopes
Keyword(s):  
Sensor Networks ◽  
Moving Average ◽  
Network Size ◽  
Streaming Data ◽  
Data Sources ◽  
Sensor Data ◽  
Local Algorithm ◽  
Network Simulator ◽  
Local Algorithms ◽  
High Level

In ubiquitous streaming data sources, such as sensor networks, clustering nodes by the data they produce gives insights on the phenomenon being monitored. However, centralized algorithms force communication and storage requirements to grow unbounded. This article presents L2GClust, an algorithm to compute local clusterings at each node as an approximation of the global clustering. L2GClust performs local clustering of the sources based on the moving average of each node’s data over time: the moving average is approximated using memory-less statistics; clustering is based on the furthest-point algorithm applied to the centroids computed by the node’s direct neighbors. Evaluation is performed both on synthetic and real sensor data, using a state-of-the-art sensor network simulator and measuring sensitivity to network size, number of clusters, cluster overlapping, and communication incompleteness. A high level of agreement was found between local and global clusterings, with special emphasis on separability agreement, while an overall robustness to incomplete communications emerged. Communication reduction was also theoretically shown, with communication ratios empirically evaluated for large networks. L2GClust is able to keep a good approximation of the global clustering, using less communication than a centralized alternative, supporting the recommendation to use local algorithms for distributed clustering of streaming data sources.

Estimating Crew Alertness From Speech

2010 ◽  
Author(s):  
Parham Shahidi ◽  
Steve C. Southward ◽  
Mehdi Ahmadian
Keyword(s):  
Fuzzy Logic ◽  
Real Time ◽  
Speech Signal ◽  
High Speed ◽  
The United States ◽  
Rule Base ◽  
Membership Functions ◽  
Fuzzy Logic Algorithm ◽  
Novel Approach ◽  
The Government

With the latest initiative of the government to develop a high speed passenger rail system in the United States the first and most important strategic transportation goal is to “Ensure safe and efficient transportation choices. A key element of safe railroad operation is to address the issue of fatigue among railroad operating employees and how to fight it. In this paper, we are presenting a novel approach to estimating fatigue levels of train conductors by analyzing the speech signal in the communication between the conductor and dispatch. We extract vocal indicators of fatigue from the speech signal and use Fuzzy Logic to generate an estimate of the mental state of the train conductor. Previous research has shown that sleeping disorders, reduced hours of rest and disrupted circadian rhythms lead to significantly increased fatigue levels which manifest themselves in alterations of speech patterns as compared to alert states of mind. To make a decision about the level of fatigue, we are proposing a Fuzzy Logic algorithm which combines inputs such as word production rate and speech intensity to generate a Fatigue Quotient at any moment in time when speech is present. The computation of the Fatigue Quotient relies on a rule base which draws from existing knowledge about fatigue indicators and their relation to the level of fatigue of the subject. For this project, the rule base and the membership functions associated with it were derived from real time testing and the subsequent tuning of parameters to refine the detection of changes in patterns. It was successfully shown that Fuzzy Logic can be implemented to estimate alertness levels from speech metrics in real-time and that the membership functions for this purpose can be found empirically through iterative testing. Furthermore, this study has proven that the framework to run such an analysis continuously as a monitoring function in locomotive cabins is feasible and can be realized with relatively inexpensive hardware.

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